{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "time-series-covid-19.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0LdAvhCnCMDL",
        "colab_type": "text"
      },
      "source": [
        "# Time Series Forecasting with LSTMs for Daily Coronavirus Cases using PyTorch in Python "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "A59PQFTSCQUg",
        "colab_type": "text"
      },
      "source": [
        "> This tutorial is NOT trying to build a model that predicts the Covid-19 outbreak/pandemic in the best way possible. This is an example of how you can use Recurrent Neural Networks on some real-world Time Series data with PyTorch. Hopefully, there are much better models that predict the number of daily confirmed cases.\n",
        "\n",
        "Time series data captures a series of data points recorded at (usually) regular intervals. Some common examples include daily weather temperature, stock prices, and the number of sales a company makes.\n",
        "\n",
        "Many classical methods (e.g. ARIMA) try to deal with Time Series data with varying success (not to say they are bad at it). In the last couple of years, [Long Short Term Memory Networks (LSTM)](https://en.wikipedia.org/wiki/Long_short-term_memory) models have become a very useful method when dealing with those types of data.\n",
        "\n",
        "Recurrent Neural Networks (LSTMs are one type of those) are very good at processing sequences of data. They can \"recall\" patterns in the data that are very far into the past (or future). In this tutorial, you're going to learn how to use LSTMs to predict future Coronavirus cases based on real-world data.\n",
        "\n",
        "- [Run the complete notebook in your browser (Google Colab)](https://colab.research.google.com/drive/1nQYJq1f7f4R0yeZOzQ9rBKgk00AfLoS0)\n",
        "- [Read the Getting Things Done with Pytorch book](https://github.com/curiousily/Getting-Things-Done-with-Pytorch)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IR387vTYWH68",
        "colab_type": "text"
      },
      "source": [
        "## Novel Coronavirus (COVID-19)\n",
        "\n",
        "The novel Coronavirus (Covid-19) has spread around the world very rapidly. At the time of this writing, [Worldometers.info](https://www.worldometers.info/coronavirus/) shows that there are more than *95,488* confirmed cases in more than *84* countries.\n",
        "\n",
        "The top 4 worst-affected (by far) are China (the source of the virus), South Korea, Italy, and Iran. Unfortunately, many cases are currently not reported due to:\n",
        "\n",
        "- A person can get infected without even knowing (asymptomatic)\n",
        "- Incorrect data reporting\n",
        "- Not enough test kits\n",
        "- The symptoms look a lot like the common flu\n",
        "\n",
        "### How dangerous is this virus?\n",
        "\n",
        "Except for the common statistics you might see cited on the news, there are some good and some bad news:\n",
        "\n",
        "- More than 80% of the confirmed cases recover without any need of medical attention\n",
        "- [3.4% Mortality Rate estimate by the World Health Organization (WHO) as of March 3](https://www.worldometers.info/coronavirus/coronavirus-death-rate/#who-03-03-20)\n",
        "- The reproductive number which represents the average number of people to which a single infected person will transmit the virus is between 1.4 and 2.5 [(WHO's estimated on Jan. 23)](https://www.worldometers.info/coronavirus/#repro)\n",
        "\n",
        "The last one is really scary. It sounds like we can witness some crazy exponential growth if appropriate measures are not put in place.\n",
        "\n",
        "Let's get started!"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W8UyCsI_mlqP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!pip install -Uq watermark"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zI0W-_9XmjY6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 125
        },
        "outputId": "774c547b-02d3-487c-88d7-d74620add20a"
      },
      "source": [
        "%reload_ext watermark\n",
        "%watermark -v -p numpy,pandas,torch"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "CPython 3.6.9\n",
            "IPython 5.5.0\n",
            "\n",
            "numpy 1.17.5\n",
            "pandas 0.25.3\n",
            "torch 1.4.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tzMzcTdKygBE",
        "colab_type": "code",
        "outputId": "7f84d0cf-a465-4f36-9805-2f5619391df9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "import torch\n",
        "\n",
        "import os\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from tqdm import tqdm\n",
        "import seaborn as sns\n",
        "from pylab import rcParams\n",
        "import matplotlib.pyplot as plt\n",
        "from matplotlib import rc\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "from pandas.plotting import register_matplotlib_converters\n",
        "from torch import nn, optim\n",
        "\n",
        "%matplotlib inline\n",
        "%config InlineBackend.figure_format='retina'\n",
        "\n",
        "sns.set(style='whitegrid', palette='muted', font_scale=1.2)\n",
        "\n",
        "HAPPY_COLORS_PALETTE = [\"#01BEFE\", \"#FFDD00\", \"#FF7D00\", \"#FF006D\", \"#93D30C\", \"#8F00FF\"]\n",
        "\n",
        "sns.set_palette(sns.color_palette(HAPPY_COLORS_PALETTE))\n",
        "\n",
        "rcParams['figure.figsize'] = 14, 10\n",
        "register_matplotlib_converters()\n",
        "\n",
        "RANDOM_SEED = 42\n",
        "np.random.seed(RANDOM_SEED)\n",
        "torch.manual_seed(RANDOM_SEED)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<torch._C.Generator at 0x7faeaa744d30>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2Jmoo1E2XM4y",
        "colab_type": "text"
      },
      "source": [
        "## Daily Cases Dataset"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jz_3S--lXK9a",
        "colab_type": "text"
      },
      "source": [
        "The data is provided by the Johns Hopkins University Center for Systems Science and Engineering (JHU CSSE) and contains the number of reported daily cases by country. [The dataset is available on GitHub](https://github.com/CSSEGISandData/COVID-19) and is updated regularly.\n",
        "\n",
        "We're going to take the Time Series data only for confirmed cases (number of deaths and recovered cases are also available):"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qspvxWyoypeX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# !wget https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_19-covid-Confirmed.csv"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F1L-crLVYq-s",
        "colab_type": "text"
      },
      "source": [
        "Or you can take the same dataset that I've used for this tutorial (the data snapshot is from 3 March 2020):"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ISbbDUFl8SvH",
        "colab_type": "code",
        "outputId": "052a3e38-ebde-42a2-b8c2-ffd565504bf3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 89
        }
      },
      "source": [
        "!gdown --id 1AsfdLrGESCQnRW5rbMz56A1KBc3Fe5aV"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=1AsfdLrGESCQnRW5rbMz56A1KBc3Fe5aV\n",
            "To: /content/time_series_19-covid-Confirmed.csv\n",
            "\r  0% 0.00/19.2k [00:00<?, ?B/s]\r100% 19.2k/19.2k [00:00<00:00, 28.6MB/s]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "L9GCMpR58kxd",
        "colab_type": "text"
      },
      "source": [
        "## Data exploration\n",
        "\n",
        "Let's load the data and have a peek:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3_15jwwrASTP",
        "colab_type": "code",
        "outputId": "beb76603-82b3-435c-a01c-0c7c0175e8c8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        }
      },
      "source": [
        "df = pd.read_csv('time_series_19-covid-Confirmed.csv')\n",
        "df.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Province/State</th>\n",
              "      <th>Country/Region</th>\n",
              "      <th>Lat</th>\n",
              "      <th>Long</th>\n",
              "      <th>1/22/20</th>\n",
              "      <th>1/23/20</th>\n",
              "      <th>1/24/20</th>\n",
              "      <th>1/25/20</th>\n",
              "      <th>1/26/20</th>\n",
              "      <th>1/27/20</th>\n",
              "      <th>1/28/20</th>\n",
              "      <th>1/29/20</th>\n",
              "      <th>1/30/20</th>\n",
              "      <th>1/31/20</th>\n",
              "      <th>2/1/20</th>\n",
              "      <th>2/2/20</th>\n",
              "      <th>2/3/20</th>\n",
              "      <th>2/4/20</th>\n",
              "      <th>2/5/20</th>\n",
              "      <th>2/6/20</th>\n",
              "      <th>2/7/20</th>\n",
              "      <th>2/8/20</th>\n",
              "      <th>2/9/20</th>\n",
              "      <th>2/10/20</th>\n",
              "      <th>2/11/20</th>\n",
              "      <th>2/12/20</th>\n",
              "      <th>2/13/20</th>\n",
              "      <th>2/14/20</th>\n",
              "      <th>2/15/20</th>\n",
              "      <th>2/16/20</th>\n",
              "      <th>2/17/20</th>\n",
              "      <th>2/18/20</th>\n",
              "      <th>2/19/20</th>\n",
              "      <th>2/20/20</th>\n",
              "      <th>2/21/20</th>\n",
              "      <th>2/22/20</th>\n",
              "      <th>2/23/20</th>\n",
              "      <th>2/24/20</th>\n",
              "      <th>2/25/20</th>\n",
              "      <th>2/26/20</th>\n",
              "      <th>2/27/20</th>\n",
              "      <th>2/28/20</th>\n",
              "      <th>2/29/20</th>\n",
              "      <th>3/1/20</th>\n",
              "      <th>3/2/20</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Anhui</td>\n",
              "      <td>Mainland China</td>\n",
              "      <td>31.8257</td>\n",
              "      <td>117.2264</td>\n",
              "      <td>1</td>\n",
              "      <td>9</td>\n",
              "      <td>15</td>\n",
              "      <td>39</td>\n",
              "      <td>60</td>\n",
              "      <td>70</td>\n",
              "      <td>106</td>\n",
              "      <td>152</td>\n",
              "      <td>200</td>\n",
              "      <td>237</td>\n",
              "      <td>297</td>\n",
              "      <td>340</td>\n",
              "      <td>408</td>\n",
              "      <td>480</td>\n",
              "      <td>530</td>\n",
              "      <td>591</td>\n",
              "      <td>665</td>\n",
              "      <td>733</td>\n",
              "      <td>779</td>\n",
              "      <td>830</td>\n",
              "      <td>860</td>\n",
              "      <td>889</td>\n",
              "      <td>910</td>\n",
              "      <td>934</td>\n",
              "      <td>950</td>\n",
              "      <td>962</td>\n",
              "      <td>973</td>\n",
              "      <td>982</td>\n",
              "      <td>986</td>\n",
              "      <td>987</td>\n",
              "      <td>988</td>\n",
              "      <td>989</td>\n",
              "      <td>989</td>\n",
              "      <td>989</td>\n",
              "      <td>989</td>\n",
              "      <td>989</td>\n",
              "      <td>989</td>\n",
              "      <td>990</td>\n",
              "      <td>990</td>\n",
              "      <td>990</td>\n",
              "      <td>990</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Beijing</td>\n",
              "      <td>Mainland China</td>\n",
              "      <td>40.1824</td>\n",
              "      <td>116.4142</td>\n",
              "      <td>14</td>\n",
              "      <td>22</td>\n",
              "      <td>36</td>\n",
              "      <td>41</td>\n",
              "      <td>68</td>\n",
              "      <td>80</td>\n",
              "      <td>91</td>\n",
              "      <td>111</td>\n",
              "      <td>114</td>\n",
              "      <td>139</td>\n",
              "      <td>168</td>\n",
              "      <td>191</td>\n",
              "      <td>212</td>\n",
              "      <td>228</td>\n",
              "      <td>253</td>\n",
              "      <td>274</td>\n",
              "      <td>297</td>\n",
              "      <td>315</td>\n",
              "      <td>326</td>\n",
              "      <td>337</td>\n",
              "      <td>342</td>\n",
              "      <td>352</td>\n",
              "      <td>366</td>\n",
              "      <td>372</td>\n",
              "      <td>375</td>\n",
              "      <td>380</td>\n",
              "      <td>381</td>\n",
              "      <td>387</td>\n",
              "      <td>393</td>\n",
              "      <td>395</td>\n",
              "      <td>396</td>\n",
              "      <td>399</td>\n",
              "      <td>399</td>\n",
              "      <td>399</td>\n",
              "      <td>400</td>\n",
              "      <td>400</td>\n",
              "      <td>410</td>\n",
              "      <td>410</td>\n",
              "      <td>411</td>\n",
              "      <td>413</td>\n",
              "      <td>414</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Chongqing</td>\n",
              "      <td>Mainland China</td>\n",
              "      <td>30.0572</td>\n",
              "      <td>107.8740</td>\n",
              "      <td>6</td>\n",
              "      <td>9</td>\n",
              "      <td>27</td>\n",
              "      <td>57</td>\n",
              "      <td>75</td>\n",
              "      <td>110</td>\n",
              "      <td>132</td>\n",
              "      <td>147</td>\n",
              "      <td>182</td>\n",
              "      <td>211</td>\n",
              "      <td>247</td>\n",
              "      <td>300</td>\n",
              "      <td>337</td>\n",
              "      <td>366</td>\n",
              "      <td>389</td>\n",
              "      <td>411</td>\n",
              "      <td>426</td>\n",
              "      <td>428</td>\n",
              "      <td>468</td>\n",
              "      <td>486</td>\n",
              "      <td>505</td>\n",
              "      <td>518</td>\n",
              "      <td>529</td>\n",
              "      <td>537</td>\n",
              "      <td>544</td>\n",
              "      <td>551</td>\n",
              "      <td>553</td>\n",
              "      <td>555</td>\n",
              "      <td>560</td>\n",
              "      <td>567</td>\n",
              "      <td>572</td>\n",
              "      <td>573</td>\n",
              "      <td>575</td>\n",
              "      <td>576</td>\n",
              "      <td>576</td>\n",
              "      <td>576</td>\n",
              "      <td>576</td>\n",
              "      <td>576</td>\n",
              "      <td>576</td>\n",
              "      <td>576</td>\n",
              "      <td>576</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Fujian</td>\n",
              "      <td>Mainland China</td>\n",
              "      <td>26.0789</td>\n",
              "      <td>117.9874</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>10</td>\n",
              "      <td>18</td>\n",
              "      <td>35</td>\n",
              "      <td>59</td>\n",
              "      <td>80</td>\n",
              "      <td>84</td>\n",
              "      <td>101</td>\n",
              "      <td>120</td>\n",
              "      <td>144</td>\n",
              "      <td>159</td>\n",
              "      <td>179</td>\n",
              "      <td>194</td>\n",
              "      <td>205</td>\n",
              "      <td>215</td>\n",
              "      <td>224</td>\n",
              "      <td>239</td>\n",
              "      <td>250</td>\n",
              "      <td>261</td>\n",
              "      <td>267</td>\n",
              "      <td>272</td>\n",
              "      <td>279</td>\n",
              "      <td>281</td>\n",
              "      <td>285</td>\n",
              "      <td>287</td>\n",
              "      <td>290</td>\n",
              "      <td>292</td>\n",
              "      <td>293</td>\n",
              "      <td>293</td>\n",
              "      <td>293</td>\n",
              "      <td>293</td>\n",
              "      <td>293</td>\n",
              "      <td>293</td>\n",
              "      <td>294</td>\n",
              "      <td>294</td>\n",
              "      <td>296</td>\n",
              "      <td>296</td>\n",
              "      <td>296</td>\n",
              "      <td>296</td>\n",
              "      <td>296</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Gansu</td>\n",
              "      <td>Mainland China</td>\n",
              "      <td>36.0611</td>\n",
              "      <td>103.8343</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>7</td>\n",
              "      <td>14</td>\n",
              "      <td>19</td>\n",
              "      <td>24</td>\n",
              "      <td>26</td>\n",
              "      <td>29</td>\n",
              "      <td>40</td>\n",
              "      <td>51</td>\n",
              "      <td>55</td>\n",
              "      <td>57</td>\n",
              "      <td>62</td>\n",
              "      <td>62</td>\n",
              "      <td>67</td>\n",
              "      <td>79</td>\n",
              "      <td>83</td>\n",
              "      <td>83</td>\n",
              "      <td>86</td>\n",
              "      <td>87</td>\n",
              "      <td>90</td>\n",
              "      <td>90</td>\n",
              "      <td>90</td>\n",
              "      <td>90</td>\n",
              "      <td>91</td>\n",
              "      <td>91</td>\n",
              "      <td>91</td>\n",
              "      <td>91</td>\n",
              "      <td>91</td>\n",
              "      <td>91</td>\n",
              "      <td>91</td>\n",
              "      <td>91</td>\n",
              "      <td>91</td>\n",
              "      <td>91</td>\n",
              "      <td>91</td>\n",
              "      <td>91</td>\n",
              "      <td>91</td>\n",
              "      <td>91</td>\n",
              "      <td>91</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Province/State  Country/Region      Lat  ...  2/29/20  3/1/20  3/2/20\n",
              "0          Anhui  Mainland China  31.8257  ...      990     990     990\n",
              "1        Beijing  Mainland China  40.1824  ...      411     413     414\n",
              "2      Chongqing  Mainland China  30.0572  ...      576     576     576\n",
              "3         Fujian  Mainland China  26.0789  ...      296     296     296\n",
              "4          Gansu  Mainland China  36.0611  ...       91      91      91\n",
              "\n",
              "[5 rows x 45 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "639__gtacb-5",
        "colab_type": "text"
      },
      "source": [
        "Two things to note here:\n",
        "\n",
        "- The data contains a province, country, latitude, and longitude. We won't be needing those.\n",
        "- The number of cases is cumulative. We'll undo the accumulation.\n",
        "\n",
        "Let's start by getting rid of the first four columns:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Lx2bxxJyWLdU",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df = df.iloc[:, 4:]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zYREMR00ZeUT",
        "colab_type": "code",
        "outputId": "e291eb25-5800-4b74-dec4-63c193898fdf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        }
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>1/22/20</th>\n",
              "      <th>1/23/20</th>\n",
              "      <th>1/24/20</th>\n",
              "      <th>1/25/20</th>\n",
              "      <th>1/26/20</th>\n",
              "      <th>1/27/20</th>\n",
              "      <th>1/28/20</th>\n",
              "      <th>1/29/20</th>\n",
              "      <th>1/30/20</th>\n",
              "      <th>1/31/20</th>\n",
              "      <th>2/1/20</th>\n",
              "      <th>2/2/20</th>\n",
              "      <th>2/3/20</th>\n",
              "      <th>2/4/20</th>\n",
              "      <th>2/5/20</th>\n",
              "      <th>2/6/20</th>\n",
              "      <th>2/7/20</th>\n",
              "      <th>2/8/20</th>\n",
              "      <th>2/9/20</th>\n",
              "      <th>2/10/20</th>\n",
              "      <th>2/11/20</th>\n",
              "      <th>2/12/20</th>\n",
              "      <th>2/13/20</th>\n",
              "      <th>2/14/20</th>\n",
              "      <th>2/15/20</th>\n",
              "      <th>2/16/20</th>\n",
              "      <th>2/17/20</th>\n",
              "      <th>2/18/20</th>\n",
              "      <th>2/19/20</th>\n",
              "      <th>2/20/20</th>\n",
              "      <th>2/21/20</th>\n",
              "      <th>2/22/20</th>\n",
              "      <th>2/23/20</th>\n",
              "      <th>2/24/20</th>\n",
              "      <th>2/25/20</th>\n",
              "      <th>2/26/20</th>\n",
              "      <th>2/27/20</th>\n",
              "      <th>2/28/20</th>\n",
              "      <th>2/29/20</th>\n",
              "      <th>3/1/20</th>\n",
              "      <th>3/2/20</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>9</td>\n",
              "      <td>15</td>\n",
              "      <td>39</td>\n",
              "      <td>60</td>\n",
              "      <td>70</td>\n",
              "      <td>106</td>\n",
              "      <td>152</td>\n",
              "      <td>200</td>\n",
              "      <td>237</td>\n",
              "      <td>297</td>\n",
              "      <td>340</td>\n",
              "      <td>408</td>\n",
              "      <td>480</td>\n",
              "      <td>530</td>\n",
              "      <td>591</td>\n",
              "      <td>665</td>\n",
              "      <td>733</td>\n",
              "      <td>779</td>\n",
              "      <td>830</td>\n",
              "      <td>860</td>\n",
              "      <td>889</td>\n",
              "      <td>910</td>\n",
              "      <td>934</td>\n",
              "      <td>950</td>\n",
              "      <td>962</td>\n",
              "      <td>973</td>\n",
              "      <td>982</td>\n",
              "      <td>986</td>\n",
              "      <td>987</td>\n",
              "      <td>988</td>\n",
              "      <td>989</td>\n",
              "      <td>989</td>\n",
              "      <td>989</td>\n",
              "      <td>989</td>\n",
              "      <td>989</td>\n",
              "      <td>989</td>\n",
              "      <td>990</td>\n",
              "      <td>990</td>\n",
              "      <td>990</td>\n",
              "      <td>990</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>14</td>\n",
              "      <td>22</td>\n",
              "      <td>36</td>\n",
              "      <td>41</td>\n",
              "      <td>68</td>\n",
              "      <td>80</td>\n",
              "      <td>91</td>\n",
              "      <td>111</td>\n",
              "      <td>114</td>\n",
              "      <td>139</td>\n",
              "      <td>168</td>\n",
              "      <td>191</td>\n",
              "      <td>212</td>\n",
              "      <td>228</td>\n",
              "      <td>253</td>\n",
              "      <td>274</td>\n",
              "      <td>297</td>\n",
              "      <td>315</td>\n",
              "      <td>326</td>\n",
              "      <td>337</td>\n",
              "      <td>342</td>\n",
              "      <td>352</td>\n",
              "      <td>366</td>\n",
              "      <td>372</td>\n",
              "      <td>375</td>\n",
              "      <td>380</td>\n",
              "      <td>381</td>\n",
              "      <td>387</td>\n",
              "      <td>393</td>\n",
              "      <td>395</td>\n",
              "      <td>396</td>\n",
              "      <td>399</td>\n",
              "      <td>399</td>\n",
              "      <td>399</td>\n",
              "      <td>400</td>\n",
              "      <td>400</td>\n",
              "      <td>410</td>\n",
              "      <td>410</td>\n",
              "      <td>411</td>\n",
              "      <td>413</td>\n",
              "      <td>414</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>6</td>\n",
              "      <td>9</td>\n",
              "      <td>27</td>\n",
              "      <td>57</td>\n",
              "      <td>75</td>\n",
              "      <td>110</td>\n",
              "      <td>132</td>\n",
              "      <td>147</td>\n",
              "      <td>182</td>\n",
              "      <td>211</td>\n",
              "      <td>247</td>\n",
              "      <td>300</td>\n",
              "      <td>337</td>\n",
              "      <td>366</td>\n",
              "      <td>389</td>\n",
              "      <td>411</td>\n",
              "      <td>426</td>\n",
              "      <td>428</td>\n",
              "      <td>468</td>\n",
              "      <td>486</td>\n",
              "      <td>505</td>\n",
              "      <td>518</td>\n",
              "      <td>529</td>\n",
              "      <td>537</td>\n",
              "      <td>544</td>\n",
              "      <td>551</td>\n",
              "      <td>553</td>\n",
              "      <td>555</td>\n",
              "      <td>560</td>\n",
              "      <td>567</td>\n",
              "      <td>572</td>\n",
              "      <td>573</td>\n",
              "      <td>575</td>\n",
              "      <td>576</td>\n",
              "      <td>576</td>\n",
              "      <td>576</td>\n",
              "      <td>576</td>\n",
              "      <td>576</td>\n",
              "      <td>576</td>\n",
              "      <td>576</td>\n",
              "      <td>576</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>10</td>\n",
              "      <td>18</td>\n",
              "      <td>35</td>\n",
              "      <td>59</td>\n",
              "      <td>80</td>\n",
              "      <td>84</td>\n",
              "      <td>101</td>\n",
              "      <td>120</td>\n",
              "      <td>144</td>\n",
              "      <td>159</td>\n",
              "      <td>179</td>\n",
              "      <td>194</td>\n",
              "      <td>205</td>\n",
              "      <td>215</td>\n",
              "      <td>224</td>\n",
              "      <td>239</td>\n",
              "      <td>250</td>\n",
              "      <td>261</td>\n",
              "      <td>267</td>\n",
              "      <td>272</td>\n",
              "      <td>279</td>\n",
              "      <td>281</td>\n",
              "      <td>285</td>\n",
              "      <td>287</td>\n",
              "      <td>290</td>\n",
              "      <td>292</td>\n",
              "      <td>293</td>\n",
              "      <td>293</td>\n",
              "      <td>293</td>\n",
              "      <td>293</td>\n",
              "      <td>293</td>\n",
              "      <td>293</td>\n",
              "      <td>294</td>\n",
              "      <td>294</td>\n",
              "      <td>296</td>\n",
              "      <td>296</td>\n",
              "      <td>296</td>\n",
              "      <td>296</td>\n",
              "      <td>296</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>7</td>\n",
              "      <td>14</td>\n",
              "      <td>19</td>\n",
              "      <td>24</td>\n",
              "      <td>26</td>\n",
              "      <td>29</td>\n",
              "      <td>40</td>\n",
              "      <td>51</td>\n",
              "      <td>55</td>\n",
              "      <td>57</td>\n",
              "      <td>62</td>\n",
              "      <td>62</td>\n",
              "      <td>67</td>\n",
              "      <td>79</td>\n",
              "      <td>83</td>\n",
              "      <td>83</td>\n",
              "      <td>86</td>\n",
              "      <td>87</td>\n",
              "      <td>90</td>\n",
              "      <td>90</td>\n",
              "      <td>90</td>\n",
              "      <td>90</td>\n",
              "      <td>91</td>\n",
              "      <td>91</td>\n",
              "      <td>91</td>\n",
              "      <td>91</td>\n",
              "      <td>91</td>\n",
              "      <td>91</td>\n",
              "      <td>91</td>\n",
              "      <td>91</td>\n",
              "      <td>91</td>\n",
              "      <td>91</td>\n",
              "      <td>91</td>\n",
              "      <td>91</td>\n",
              "      <td>91</td>\n",
              "      <td>91</td>\n",
              "      <td>91</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   1/22/20  1/23/20  1/24/20  1/25/20  ...  2/28/20  2/29/20  3/1/20  3/2/20\n",
              "0        1        9       15       39  ...      990      990     990     990\n",
              "1       14       22       36       41  ...      410      411     413     414\n",
              "2        6        9       27       57  ...      576      576     576     576\n",
              "3        1        5       10       18  ...      296      296     296     296\n",
              "4        0        2        2        4  ...       91       91      91      91\n",
              "\n",
              "[5 rows x 41 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hdBlnURyXVgw",
        "colab_type": "text"
      },
      "source": [
        "Let's check for missing values:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8yb8Ov_Ia5Se",
        "colab_type": "code",
        "outputId": "5ea6a354-d60a-460b-eb33-df4bd7468ffa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "df.isnull().sum().sum()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hqolOGG3cZcT",
        "colab_type": "text"
      },
      "source": [
        "Everything seems to be in place. Let's sum all rows, so we get the cumulative daily cases:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DbVKPyMybITz",
        "colab_type": "code",
        "outputId": "e84a4ba5-d34c-4f65-eb92-467ba28c3104",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 125
        }
      },
      "source": [
        "daily_cases = df.sum(axis=0)\n",
        "daily_cases.index = pd.to_datetime(daily_cases.index)\n",
        "daily_cases.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2020-01-22     555\n",
              "2020-01-23     653\n",
              "2020-01-24     941\n",
              "2020-01-25    1434\n",
              "2020-01-26    2118\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QyTmkP2OQLui",
        "colab_type": "code",
        "outputId": "3c70298c-36ba-448d-e551-4759d3a277ea",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 614
        }
      },
      "source": [
        "plt.plot(daily_cases)\n",
        "plt.title(\"Cumulative daily cases\");"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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za5MRw4RLAFCOhE4AAAAAQ0hRFHm+MV23xFvX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\niU4A0EstXFfk04uTH7zUfr66I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            "text/plain": [
              "<Figure size 1008x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "image/png": {
              "width": 846,
              "height": 597
            }
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K4VmZuoyeAiZ",
        "colab_type": "text"
      },
      "source": [
        "We'll undo the accumulation by subtracting the current value from the previous. We'll preserve the first value of the sequence:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Lb-6wPBYb1uG",
        "colab_type": "code",
        "outputId": "4079bbfe-5381-430e-9834-b0c2b76235d0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 125
        }
      },
      "source": [
        "daily_cases = daily_cases.diff().fillna(daily_cases[0]).astype(np.int64)\n",
        "daily_cases.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2020-01-22    555\n",
              "2020-01-23     98\n",
              "2020-01-24    288\n",
              "2020-01-25    493\n",
              "2020-01-26    684\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KVXZ7tCsQvHX",
        "colab_type": "code",
        "outputId": "160b929f-7c9f-4b68-bdc6-eed2565ed283",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 614
        }
      },
      "source": [
        "plt.plot(daily_cases)\n",
        "plt.title(\"Daily cases\");"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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H8BXbsGFDrr766mzatGlXbrmFM844IwMGDEiS/P3f/31z4PEXf/EXJdc+//zzM3jw4CTJ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\nKfBn//HJ0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ZNKuHol3LrGUh+Mje9BHMp0irwBWtNJRERERDqAgk4iIiLiS7saLFtCM61T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axhg9/fTTkuzB2H7+85+3etvOIiIiNGXKFEnSqlWrtHHjxkbLzJs3z5GHs/bY+Iq7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\nt6XfAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 1008x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "image/png": {
              "width": 846,
              "height": 597
            }
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_KX1spUAgIcy",
        "colab_type": "text"
      },
      "source": [
        "The huge spike (in the middle) is mostly due to a change of criteria for testing patients in China. This will certainly be a challenge for our model.\n",
        "\n",
        "Let's check the amount of data we have:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ij7GhQKsN094",
        "colab_type": "code",
        "outputId": "7a4ed8bc-617d-434d-e8a7-4d1fe57d61f1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "daily_cases.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(41,)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qjFl0n8oki8I",
        "colab_type": "text"
      },
      "source": [
        "Unfortunately, we have data for only 41 days. Let's see what we can do with it!"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "T4ONc718zaAW",
        "colab_type": "text"
      },
      "source": [
        "## Preprocessing\n",
        "\n",
        "We'll reserve the first 27 days for training and use the rest for testing:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4xsvSUYFRwkk",
        "colab_type": "code",
        "outputId": "f606dd42-97be-4b8b-f8da-ef1700a42361",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "test_data_size = 14\n",
        "\n",
        "train_data = daily_cases[:-test_data_size]\n",
        "test_data = daily_cases[-test_data_size:]\n",
        "\n",
        "train_data.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(27,)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i8SRrEWSZujN",
        "colab_type": "text"
      },
      "source": [
        "We have to scale the data (values will be between 0 and 1) if we want to increase the training speed and performance of the model. We'll use the `MinMaxScaler` from scikit-learn:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZMKl_mSZUENB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "scaler = MinMaxScaler()\n",
        "\n",
        "scaler = scaler.fit(np.expand_dims(train_data, axis=1))\n",
        "\n",
        "train_data = scaler.transform(np.expand_dims(train_data, axis=1))\n",
        "\n",
        "test_data = scaler.transform(np.expand_dims(test_data, axis=1))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-KD-OgmQbdac",
        "colab_type": "text"
      },
      "source": [
        "Currently, we have a big sequence of daily cases. We'll convert it into smaller ones:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tN7P8h-uHSKV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def create_sequences(data, seq_length):\n",
        "    xs = []\n",
        "    ys = []\n",
        "\n",
        "    for i in range(len(data)-seq_length-1):\n",
        "        x = data[i:(i+seq_length)]\n",
        "        y = data[i+seq_length]\n",
        "        xs.append(x)\n",
        "        ys.append(y)\n",
        "\n",
        "    return np.array(xs), np.array(ys)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "92DqpTj8IxDI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "seq_length = 5\n",
        "X_train, y_train = create_sequences(train_data, seq_length)\n",
        "X_test, y_test = create_sequences(test_data, seq_length)\n",
        "\n",
        "X_train = torch.from_numpy(X_train).float()\n",
        "y_train = torch.from_numpy(y_train).float()\n",
        "\n",
        "X_test = torch.from_numpy(X_test).float()\n",
        "y_test = torch.from_numpy(y_test).float()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EBN2MIinc2FN",
        "colab_type": "text"
      },
      "source": [
        "Each training example contains a sequence of 5 data points of history and a label for the real value that our model needs to predict. Let's dive in:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5Db7NMt1haaw",
        "colab_type": "code",
        "outputId": "f3141da7-fd47-4692-df43-71f36a370674",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "X_train.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([21, 5, 1])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nouTrj7Vhd3i",
        "colab_type": "code",
        "outputId": "48602534-c809-4552-afc8-bc274170c325",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 215
        }
      },
      "source": [
        "X_train[:2]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[[0.0304],\n",
              "         [0.0000],\n",
              "         [0.0126],\n",
              "         [0.0262],\n",
              "         [0.0389]],\n",
              "\n",
              "        [[0.0000],\n",
              "         [0.0126],\n",
              "         [0.0262],\n",
              "         [0.0389],\n",
              "         [0.0472]]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eSi-I0L1hiDB",
        "colab_type": "code",
        "outputId": "79e67bda-9868-47fa-afbe-e49f6d002784",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "y_train.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([21, 1])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-fDsqZD6hjF8",
        "colab_type": "code",
        "outputId": "8084d8e7-45e3-4b58-ae2f-2676bd984fc0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        }
      },
      "source": [
        "y_train[:2]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[0.0472],\n",
              "        [0.1696]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QcrktghWhj_I",
        "colab_type": "code",
        "outputId": "2542dd1e-4e4d-4903-bc3f-ddab71d82654",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 197
        }
      },
      "source": [
        "train_data[:10]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.03036545],\n",
              "       [0.        ],\n",
              "       [0.01262458],\n",
              "       [0.02624585],\n",
              "       [0.03893688],\n",
              "       [0.04724252],\n",
              "       [0.16963455],\n",
              "       [0.03255814],\n",
              "       [0.13089701],\n",
              "       [0.10598007]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "egnpqcZwP59F",
        "colab_type": "text"
      },
      "source": [
        "## Building a model\n",
        "\n",
        "We'll encapsulate the complexity of our model into a class that extends from `torch.nn.Module`:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vS1uFo4-qVT2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class CoronaVirusPredictor(nn.Module):\n",
        "\n",
        "  def __init__(self, n_features, n_hidden, seq_len, n_layers=2):\n",
        "    super(CoronaVirusPredictor, self).__init__()\n",
        "\n",
        "    self.n_hidden = n_hidden\n",
        "    self.seq_len = seq_len\n",
        "    self.n_layers = n_layers\n",
        "\n",
        "    self.lstm = nn.LSTM(\n",
        "      input_size=n_features,\n",
        "      hidden_size=n_hidden,\n",
        "      num_layers=n_layers,\n",
        "      dropout=0.5\n",
        "    )\n",
        "\n",
        "    self.linear = nn.Linear(in_features=n_hidden, out_features=1)\n",
        "\n",
        "  def reset_hidden_state(self):\n",
        "    self.hidden = (\n",
        "        torch.zeros(self.n_layers, self.seq_len, self.n_hidden),\n",
        "        torch.zeros(self.n_layers, self.seq_len, self.n_hidden)\n",
        "    )\n",
        "\n",
        "  def forward(self, sequences):\n",
        "    lstm_out, self.hidden = self.lstm(\n",
        "      sequences.view(len(sequences), self.seq_len, -1),\n",
        "      self.hidden\n",
        "    )\n",
        "    last_time_step = \\\n",
        "      lstm_out.view(self.seq_len, len(sequences), self.n_hidden)[-1]\n",
        "    y_pred = self.linear(last_time_step)\n",
        "    return y_pred"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xljmrTGUfduG",
        "colab_type": "text"
      },
      "source": [
        "Our `CoronaVirusPredictor` contains 3 methods:\n",
        "- constructor - initialize all helper data and create the layers\n",
        "- `reset_hidden_state` - we'll use a stateless LSTM, so we need to reset the state after each example\n",
        "- `forward` - get the sequences, pass all of them through the LSTM layer, at once. We take the output of the last time step and pass it through our linear layer to get the prediction."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FuggK-kC9BMQ",
        "colab_type": "text"
      },
      "source": [
        "## Training\n",
        "\n",
        "Let's build a helper function for the training of our model (we'll reuse it later):"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vsuBhEJ1fLnM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def train_model(\n",
        "  model, \n",
        "  train_data, \n",
        "  train_labels, \n",
        "  test_data=None, \n",
        "  test_labels=None\n",
        "):\n",
        "  loss_fn = torch.nn.MSELoss(reduction='sum')\n",
        "\n",
        "  optimiser = torch.optim.Adam(model.parameters(), lr=1e-3)\n",
        "  num_epochs = 60\n",
        "\n",
        "  train_hist = np.zeros(num_epochs)\n",
        "  test_hist = np.zeros(num_epochs)\n",
        "\n",
        "  for t in range(num_epochs):\n",
        "    model.reset_hidden_state()\n",
        "\n",
        "    y_pred = model(X_train)\n",
        "\n",
        "    loss = loss_fn(y_pred.float(), y_train)\n",
        "\n",
        "    if test_data is not None:\n",
        "      with torch.no_grad():\n",
        "        y_test_pred = model(X_test)\n",
        "        test_loss = loss_fn(y_test_pred.float(), y_test)\n",
        "      test_hist[t] = test_loss.item()\n",
        "\n",
        "      if t % 10 == 0:  \n",
        "        print(f'Epoch {t} train loss: {loss.item()} test loss: {test_loss.item()}')\n",
        "    elif t % 10 == 0:\n",
        "      print(f'Epoch {t} train loss: {loss.item()}')\n",
        "\n",
        "    train_hist[t] = loss.item()\n",
        "    \n",
        "    optimiser.zero_grad()\n",
        "\n",
        "    loss.backward()\n",
        "\n",
        "    optimiser.step()\n",
        "  \n",
        "  return model.eval(), train_hist, test_hist"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tBWwvJ8umKyw",
        "colab_type": "text"
      },
      "source": [
        "Note that the hidden state is reset at the start of each epoch. We don't use batches of data our model sees every example at once. We'll use mean squared error to measure our training and test error. We'll record both. \n",
        "\n",
        "Let's create an instance of our model and train it:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6f_ebtcBqaoK",
        "colab_type": "code",
        "outputId": "4dfba810-b81c-498b-8426-85ba9a33f2c8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 125
        }
      },
      "source": [
        "model = CoronaVirusPredictor(\n",
        "  n_features=1, \n",
        "  n_hidden=512, \n",
        "  seq_len=seq_length, \n",
        "  n_layers=2\n",
        ")\n",
        "model, train_hist, test_hist = train_model(\n",
        "  model, \n",
        "  X_train, \n",
        "  y_train, \n",
        "  X_test, \n",
        "  y_test\n",
        ")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 0 train loss: 1.6297188997268677 test loss: 0.041186608374118805\n",
            "Epoch 10 train loss: 0.8466923832893372 test loss: 0.12416432797908783\n",
            "Epoch 20 train loss: 0.8219934105873108 test loss: 0.1438201516866684\n",
            "Epoch 30 train loss: 0.8200693726539612 test loss: 0.2190694659948349\n",
            "Epoch 40 train loss: 0.810839056968689 test loss: 0.1797715127468109\n",
            "Epoch 50 train loss: 0.795730471611023 test loss: 0.19855864346027374\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cL-9E4wLmnzR",
        "colab_type": "text"
      },
      "source": [
        "Let's have a look at the train and test loss:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gkqy6CNh9AqC",
        "colab_type": "code",
        "outputId": "657e3154-4059-4bfc-d4f3-169714f1fe43",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 602
        }
      },
      "source": [
        "plt.plot(train_hist, label=\"Training loss\")\n",
        "plt.plot(test_hist, label=\"Test loss\")\n",
        "plt.ylim((0, 5))\n",
        "plt.legend();"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<Figure size 1008x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "image/png": {
              "width": 813,
              "height": 585
            }
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_pKZ7Js8m60H",
        "colab_type": "text"
      },
      "source": [
        "Our model's performance doesn't improve after 15 epochs or so. Recall that we have very little data. Maybe we shouldn't trust our model that much?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Weyp3LRBGEvv",
        "colab_type": "text"
      },
      "source": [
        "## Predicting daily cases\n",
        "\n",
        "Our model can (due to the way we've trained it) predict only a single day in the future. We'll employ a simple strategy to overcome this limitation. Use predicted values as input for predicting the next days:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6RTIMgBZ_hG_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "with torch.no_grad():\n",
        "  test_seq = X_test[:1]\n",
        "  preds = []\n",
        "  for _ in range(len(X_test)):\n",
        "    y_test_pred = model(test_seq)\n",
        "    pred = torch.flatten(y_test_pred).item()\n",
        "    preds.append(pred)\n",
        "    new_seq = test_seq.numpy().flatten()\n",
        "    new_seq = np.append(new_seq, [pred])\n",
        "    new_seq = new_seq[1:]\n",
        "    test_seq = torch.as_tensor(new_seq).view(1, seq_length, 1).float()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YBn9VijrubKS",
        "colab_type": "text"
      },
      "source": [
        "We have to reverse the scaling of the test data and the model predictions:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iPJMdlBEErg3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "true_cases = scaler.inverse_transform(\n",
        "    np.expand_dims(y_test.flatten().numpy(), axis=0)\n",
        ").flatten()\n",
        "\n",
        "predicted_cases = scaler.inverse_transform(\n",
        "  np.expand_dims(preds, axis=0)\n",
        ").flatten()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bIaUwP2gue1I",
        "colab_type": "text"
      },
      "source": [
        "Let's look at the results:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L_2LezBrE8VF",
        "colab_type": "code",
        "outputId": "50b2de93-3f79-46a5-a9f0-f82545dda382",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 597
        }
      },
      "source": [
        "plt.plot(\n",
        "  daily_cases.index[:len(train_data)], \n",
        "  scaler.inverse_transform(train_data).flatten(),\n",
        "  label='Historical Daily Cases'\n",
        ")\n",
        "\n",
        "plt.plot(\n",
        "  daily_cases.index[len(train_data):len(train_data) + len(true_cases)], \n",
        "  true_cases,\n",
        "  label='Real Daily Cases'\n",
        ")\n",
        "\n",
        "plt.plot(\n",
        "  daily_cases.index[len(train_data):len(train_data) + len(true_cases)], \n",
        "  predicted_cases, \n",
        "  label='Predicted Daily Cases'\n",
        ")\n",
        "\n",
        "plt.legend();"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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S06ZNbfal3U+NGjWyDKLCwsKs5+cHHx8f6zSI69atkyT5+flJSp1q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\nVkbpGQV0gbpnpnPnzm3UW2hDFe9i9cYbb7R63uu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Hl4dRl112WWpra3P66ae3ubHa0KFD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\nyOzBTvuJHVD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y2Xz+lk0+OySbdQVSNr3xfih0/vz58095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\n",
            "text/plain": [
              "<Figure size 1008x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "image/png": {
              "width": 849,
              "height": 580
            }
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dVXs97Onu9VV",
        "colab_type": "text"
      },
      "source": [
        "As expected, our model doesn't perform very well. That said, the predictions seem to be in the right ballpark (probably due to using the last data point as a strong predictor for the next)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cmX5Cq_aGA2s",
        "colab_type": "text"
      },
      "source": [
        "## Use all data for training\n",
        "\n",
        "Now, we'll use all available data to train the same model:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WbKlhXMuGhco",
        "colab_type": "code",
        "outputId": "cffae6d3-a929-4c95-edb5-970c5f141a97",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "scaler = MinMaxScaler()\n",
        "\n",
        "scaler = scaler.fit(np.expand_dims(daily_cases, axis=1))\n",
        "\n",
        "all_data = scaler.transform(np.expand_dims(daily_cases, axis=1))\n",
        "\n",
        "all_data.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(41, 1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9QzF2RC4YIA6",
        "colab_type": "text"
      },
      "source": [
        "The preprocessing and training steps are the same:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "toqibTKDGzzm",
        "colab_type": "code",
        "outputId": "773ac4f1-63f9-4598-953b-74012b1d206f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 125
        }
      },
      "source": [
        "X_all, y_all = create_sequences(all_data, seq_length)\n",
        "\n",
        "X_all = torch.from_numpy(X_all).float()\n",
        "y_all = torch.from_numpy(y_all).float()\n",
        "\n",
        "model = CoronaVirusPredictor(\n",
        "  n_features=1, \n",
        "  n_hidden=512, \n",
        "  seq_len=seq_length, \n",
        "  n_layers=2\n",
        ")\n",
        "model, train_hist, _ = train_model(model, X_all, y_all)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 0 train loss: 1.9441421031951904\n",
            "Epoch 10 train loss: 0.8385428786277771\n",
            "Epoch 20 train loss: 0.8256545066833496\n",
            "Epoch 30 train loss: 0.8023681640625\n",
            "Epoch 40 train loss: 0.8125611543655396\n",
            "Epoch 50 train loss: 0.8225002884864807\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Uw4JkktB9ceM",
        "colab_type": "text"
      },
      "source": [
        "## Predicting future cases\n",
        "\n",
        "We'll use our \"fully trained\" model to predict the confirmed cases for 12 days into the future:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JBEHtvM9HbCi",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "DAYS_TO_PREDICT = 12\n",
        "\n",
        "with torch.no_grad():\n",
        "  test_seq = X_all[:1]\n",
        "  preds = []\n",
        "  for _ in range(DAYS_TO_PREDICT):\n",
        "    y_test_pred = model(test_seq)\n",
        "    pred = torch.flatten(y_test_pred).item()\n",
        "    preds.append(pred)\n",
        "    new_seq = test_seq.numpy().flatten()\n",
        "    new_seq = np.append(new_seq, [pred])\n",
        "    new_seq = new_seq[1:]\n",
        "    test_seq = torch.as_tensor(new_seq).view(1, seq_length, 1).float()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CPO4SgsFZqS7",
        "colab_type": "text"
      },
      "source": [
        "As before, we'll inverse the scaler transformation:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Hq7oUHf7H8nU",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "predicted_cases = scaler.inverse_transform(\n",
        "  np.expand_dims(preds, axis=0)\n",
        ").flatten()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WEXs4wOPayG2",
        "colab_type": "text"
      },
      "source": [
        "To create a cool chart with the historical and predicted cases, we need to extend the date index of our data frame:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0Dz4tBTeEb7r",
        "colab_type": "code",
        "outputId": "3c038c12-818c-4c6e-8230-af1be0e3ee52",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "daily_cases.index[-1]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Timestamp('2020-03-02 00:00:00')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZwQxZz3BDrlI",
        "colab_type": "code",
        "outputId": "8c0855e1-0e60-4015-a54f-1d340c8f6e70",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 597
        }
      },
      "source": [
        "predicted_index = pd.date_range(\n",
        "  start=daily_cases.index[-1],\n",
        "  periods=DAYS_TO_PREDICT + 1,\n",
        "  closed='right'\n",
        ")\n",
        "\n",
        "predicted_cases = pd.Series(\n",
        "  data=predicted_cases,\n",
        "  index=predicted_index\n",
        ")\n",
        "\n",
        "plt.plot(predicted_cases, label='Predicted Daily Cases')\n",
        "plt.legend();"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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mTcu+/x46dOhe67tjx45Mnz6d1NRU3n77bRYsWMCGDRvIzc0lPj6eli1bcvTR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\npb+3bE8T55EWj3a3HgAAMPAsy9IlsdIlsdJdY6S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rvHvn37kiSjR48+6nljxozpqvfu3dvjNY41CXW0a/SVlpaWbNiwoV+uPdT5dxlc7m+tTTIrSfJb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\noaz9AAAAAADQdwmNo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            "text/plain": [
              "<Figure size 1008x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "image/png": {
              "width": 838,
              "height": 580
            }
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qBK71BNma8ec",
        "colab_type": "text"
      },
      "source": [
        "Now we can use all the data to plot the results:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XyBC8y5FFBVX",
        "colab_type": "code",
        "outputId": "34719970-a190-4435-8ed9-c086bf6e5a29",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 597
        }
      },
      "source": [
        "plt.plot(daily_cases, label='Historical Daily Cases')\n",
        "plt.plot(predicted_cases, label='Predicted Daily Cases')\n",
        "plt.legend();"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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QkFCkfkpC9+7d9dFHHyklJUWrV69W//79zceWL1+uzMxMOTs7q2vX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            "text/plain": [
              "<Figure size 1008x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "image/png": {
              "width": 863,
              "height": 580
            }
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p6IgK4dTbhQL",
        "colab_type": "text"
      },
      "source": [
        "Our model thinks that things will level off. Note that the more you go into the future, the more you shouldn't trust your model predictions."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tVUVqYlPbh63",
        "colab_type": "text"
      },
      "source": [
        "## Conclusion\n",
        "\n",
        "Well done! You learned how to use PyTorch to create a Recurrent Neural Network that works with Time Series data. The model performance is not that great, but this is expected, given the small amounts of data.\n",
        "\n",
        "- [Run the complete notebook in your browser (Google Colab)](https://colab.research.google.com/drive/1nQYJq1f7f4R0yeZOzQ9rBKgk00AfLoS0)\n",
        "- [Read the Getting Things Done with Pytorch book](https://github.com/curiousily/Getting-Things-Done-with-Pytorch)\n",
        "\n",
        "The problem of predicting daily Covid-19 cases is a hard one. We're amidst an outbreak, and there's more to be done. Hopefully, everything will be back to normal after some time."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ycqlac8X-9q-",
        "colab_type": "text"
      },
      "source": [
        "## References\n",
        "\n",
        "- [Sequence Models PyTorch Tutorial](https://pytorch.org/tutorials/beginner/nlp/sequence_models_tutorial.html)\n",
        "- [LSTM for time series prediction](https://towardsdatascience.com/lstm-for-time-series-prediction-de8aeb26f2ca)\n",
        "- [Time Series Prediction using LSTM with PyTorch in Python](https://stackabuse.com/time-series-prediction-using-lstm-with-pytorch-in-python/)\n",
        "- [Stateful LSTM in Keras](https://philipperemy.github.io/keras-stateful-lstm/)\n",
        "- [LSTMs for Time Series in PyTorch](https://www.jessicayung.com/lstms-for-time-series-in-pytorch/)\n",
        "- [Novel Coronavirus (COVID-19) Cases, provided by JHU CSSE](https://github.com/CSSEGISandData/COVID-19)\n",
        "- [covid-19-analysis](https://github.com/AaronWard/covid-19-analysis)\n",
        "- [How does Coronavirus compare to Ebola, SARS, etc?](https://www.youtube.com/watch?v=6dDD2tHWWnU)\n",
        "- [Worldometer COVID-19 Coronavirus Outbreak](https://www.worldometers.info/coronavirus/)\n",
        "- [How contagious is the Wuhan Coronavirus? (Ro)](https://www.worldometers.info/coronavirus/#repro)\n",
        "- [Systemic Risk of Pandemic via Novel Pathogens - Coronavirus: A Note](https://www.academia.edu/41743064/Systemic_Risk_of_Pandemic_via_Novel_Pathogens_-_Coronavirus_A_Note)\n",
        "- [Statistical Consequences of Fat Tails: Real World Preasymptotics, Epistemology, and Applications](https://www.researchers.one/article/2020-01-21)\n"
      ]
    }
  ]
}